Glycemic health metric determination and application

ABSTRACT

Disclosed are methods, apparatuses, etc. for determination and application of a unidimensional metric for assessing a patient&#39;s glycemic health. In one particular implementation, a computed metric may be used to balance short-term and long-term risks associated with a particular therapy. In another implementation, a computed unidimensional metric may be used to balance risks of hyperglycemia and hypoglycemia.

This application claims the benefit of priority to U.S. ProvisionalPatent Appl. No. 61/428,066 titled “Quantifying Glycemic Control UsingLog-Square Metric,” filed on Dec. 29, 2010, assigned to the assignee ofclaimed subject matter, and incorporated herein by reference in itsentirety.

BACKGROUND

1. Field

Subject matter disclosed herein relates to techniques to determine ametric quantifying a glycemic health of a patient.

2. Information

The pancreas of a normal healthy person produces and releases insulininto the blood stream in response to elevated blood plasma glucoselevels. Beta cells (β-cells), which reside in the pancreas, produce andsecrete insulin into the blood stream as it is needed. If β-cells becomeincapacitated or die, which is a condition known as Type 1 diabetesmellitus (or in some cases, if β-cells produce insufficient quantitiesof insulin, a condition known as Type 2 diabetes), then insulin may beprovided to a body from another source to maintain life or health.

Traditionally, because insulin cannot be taken orally, insulin has beeninjected with a syringe. More recently, the use of infusion pump therapyhas been increasing in a number of medical situations, including fordelivering insulin to diabetic individuals. For example, externalinfusion pumps may be worn on a belt, in a pocket, or the like, and theycan deliver insulin into a body via an infusion tube with a percutaneousneedle or a cannula placed in subcutaneous tissue.

To determine an appropriate therapy for treating a patient's diabeticconditions, a blood glucose concentration is typically measured usingone or more techniques such as, for example, metered blood glucosemeasurements (e.g. using finger sticks) or continuous glucose monitoringfrom processing signals generated by a blood glucose sensor insertedinto subcutaneous tissue. While contemporaneous measurements of bloodglucose concentration may be an effective metric for determining anappropriate therapy for addressing an immediate condition (e.g.,determining a size of an insulin bolus to be given to a patient),measurements of blood glucose alone do not necessarily provide anindication of a patient's glycemic health over a time period, forexample. Other metrics for assessing a patients' overall glycemic healthmay include a measurement of hemoglobin A1c (or HbA1c), which is oneform of glycohemoglobin. Here, such a hemoglobin is irreversiblyglycated at one or both N-terminal valine residues of a β-chain ofhemoglobin A0. Glycation of hemoglobin in a patient is typicallyquantified as a percentage of total hemoglobin.

A strong relationship exists between hemoglobin A1c levels in a diabetespatient and risks of micro-vascular complications. Accordingly,hemoglobin A1c measurements have become an integral component of thetreatment of diabetes patients.

SUMMARY

Briefly, example embodiments may relate to methods, systems,apparatuses, and/or articles, etc. for a method comprising: at a specialpurpose computing apparatus, computing a profile of a blood glucoseconcentration of a patient based, at least in part, on observations ofsaid blood glucose concentration collected at a blood glucose monitoringdevice; applying a cost or loss function to said computed profile tocompute a metric representative of a glycemic health of the patient,said cost or loss function being based, at least in part, on alog-square metric; and affecting a therapy applied to said patientbased, at least in part, on said computed metric. The computed metricmay comprise a unidimensional metric in a particular embodiment. Inanother embodiment, affecting the therapy may comprise setting a targetblood glucose level or target blood glucose range of said patient based,at least in part, on the computed metric. In one alternativeimplementation, the method may further comprise triggering an alarm inresponse to said computed metric.

In another implementation, the blood glucose concentration profile maycomprise an estimated probability distribution function. Alternatively,the blood glucose concentration profile may comprise a histogram ofblood glucose concentration measurements. In yet another alternative,the blood glucose concentration profile may comprise blood glucoseconcentration measurements. In another implementation, the log-squaremetric has the form:

${g(x)} = \left\lbrack {\log \left( \frac{x}{G_{c}} \right)} \right\rbrack^{2}$

In another implementation, the computed metric may comprise abi-dimensional metric, and the method may further comprise: applying afirst portion of said loss or cost function to said blood glucoseconcentration profile to derive a hyperglycemia component of saidcomputed metric; and applying a second portion of said loss or costfunction to said blood glucose concentration to derive a hypoglycemiacomponent of said computed metric.

In another implementation, the method further comprises: defining theblood glucose concentration profile for a recurring period; computingthe metric based, at least in part, on the recurring period; andaffecting the therapy for the recurring period in the future based, atleast in part, on the computed metric. Here, the recurring period maycomprise a time of day. Alternatively, the recurring period may comprisea day of week. In another embodiment, defining the blood glucoseconcentration profile for a recurring period further comprises computingthe profile for the recurring period based on observations of said bloodglucose concentration collected only during said recurring period.

In a particular implementation, affecting the therapy applied to saidpatient further comprises affecting a closed-loop insulin deliverysystem based, at least in part, on said computed metric. For example,affecting the closed-loop insulin delivery system may further compriseaffecting periodic command based, at least in part, on the computedmetric.

In another implementation, an apparatus may comprise one or moreprocessors to: for a plurality of patients, determine an associatedplurality of unidimensional metrics indicative of glycemic health of thepatients, said unidimensional metrics being computed based, at least inpart, on an application of a cost or loss function to profiles of bloodglucose concentration of said patients; and rank the patients fortreatment according to a triage policy based, at least in part, on theunidimensional metrics. In one alternative aspect, the apparatus mayfurther comprise communication interface components to receiveinformation from a communication network, the one or more processorsfurther to: compute said unidimensional metrics based, at least in part,on messages received through said communication interface componentsfrom computing platforms co-located with said patients. In a particularimplementation, the messages may comprise measurements of blood glucoseconcentration collected at glucose monitoring devices. Alternatively,the messages may comprise unidimensional metrics computed at thecomputing platforms co-located with the patients.

In another embodiment, an article comprises a non-transitory storagemedium comprising machine-readable instructions stored thereon which areexecutable by a special purpose computing apparatus to: determine anassociated plurality of unidimensional metrics indicative of glycemichealth of the patients, the unidimensional metrics being computed based,at least in part, on an application of a cost or loss function toprofiles of blood glucose concentration of the patients; and rank thepatients for treatment according to a triage policy based, at least inpart, on the unidimensional metrics.

In another embodiment, an article comprises a non-transitory storagemedium comprising machine-readable instructions stored thereon which areexecutable by a special purpose computing apparatus to: compute aprofile of a blood glucose concentration of a patient based, at least inpart, on observations of the blood glucose concentration collected at ablood glucose monitoring device; apply a cost or loss function to thecomputed profile to compute a metric representative of a glycemic healthof the patient, the cost or loss function being based, at least in part,on a log-square metric; and affect a therapy applied to the patientbased, at least in part, on the computed metric. In a particularimplementation, the instructions are executable by the special purposecomputing apparatus to affect the therapy by generating commands in aninfusion system.

In another embodiment, an article comprises a non-transitory storagemedium comprising machine-readable instructions stored thereon which areexecutable by a special purpose computing apparatus to: for a pluralityof patients, determine an associated plurality of unidimensional metricsindicative of glycemic health of the patients, the unidimensionalmetrics being computed based, at least in part, on an application of acost or loss function to profiles of blood glucose concentration of saidpatients; and rank the patients for treatment according to a triagepolicy based, at least in part, on said unidimensional metrics.

In another implementation, an apparatus comprises: means for computing aprofile of a blood glucose concentration of a patient based, at least inpart, on observations of said blood glucose concentration; means forapplying a cost or loss function to said computed profile to compute ametric representative of a glycemic health of the patient, the cost orloss function being based, at least in part, on a log-square metric; andmeans for affecting a therapy applied to said patient based, at least inpart, on the computed metric.

In another embodiment, an apparatus comprises: for a plurality ofpatients, means for determining an associated plurality ofunidimensional metrics indicative of glycemic health of the patients,said unidimensional metrics being computed based, at least in part, onan application of a cost or loss function to profiles of blood glucoseconcentration of the patients; and means for ranking the patients fortreatment according to a triage policy based, at least in part, on theunidimensional metrics.

In another embodiment, a method comprises: at a special purposecomputing apparatus, for a plurality of patients, determining anassociated plurality of unidimensional metrics indicative of glycemichealth of the patients, the unidimensional metrics being computed based,at least in part, on an application of a cost or loss function toprofiles of blood glucose concentration of the patients, the profiles ofblood glucose concentration being obtained for the patients undermultiple predefined therapies; and ranking the predefined therapiesbased, at least in part, on the unidimensional metrics. In oneparticular implementation, the predefined therapies are defined, atleast in part, by closed-loop system design features.

Other alternative example embodiments are described herein and/orillustrated in the accompanying Drawings. Additionally, particularexample embodiments may be directed to an article comprising a storagemedium including machine-readable instructions stored thereon which, ifexecuted by a special purpose computing device and/or processor, may bedirected to enable the special purpose computing device/processor toexecute at least a portion of described method(s) according to one ormore particular implementations. In other particular exampleembodiments, a sensor may be adapted to generate one or more signalsresponsive to a measured blood glucose concentration in a body while aspecial purpose computing device and/or processor may be adapted toperform at least a portion of described method(s) according to one ormore particular implementations based upon the one or more signalsgenerated by the sensor.

BRIEF DESCRIPTION OF THE FIGURES

Non-limiting and non-exhaustive features are described with reference tothe following figures, wherein like reference numerals refer to likeand/or analogous parts throughout the various figures:

FIG. 1 is table showing Spearman ranking coefficients according to anembodiment;

FIG. 2 is a plot of a profile of a blood glucose concentration laid overa cost or loss function according to an embodiment;

FIG. 3 is a plot of a probability density function on a logarithmicscale laid over a cost or loss function formulated as a log-squaremetric according to an embodiment;

FIG. 4 is a table of alternative cost or loss functions applicable to ablood glucose concentration profile for computing a metric indicative ofglycemic health according to alternative embodiments;

FIG. 5 is a table of Spearman rank coefficients for use in balancingthree hypoglycemic-biased metrics and three hyperglycemic-biased metricsfor use in determining a cost or loss function according to anembodiment;

FIGS. 6( a) through 6(d) are plots illustrating application of a loss orcost function to blood glucose concentration profiles of a population ofpatients according to an embodiment;

FIG. 7 is a schematic diagram of a system for managing the glycemichealth of multiple patients according to an embodiment;

FIG. 8 is a schematic diagram of an example closed loop glucose controlsystem in accordance with an embodiment.

FIG. 9 is a front view of example closed loop hardware located on a bodyin accordance with an embodiment.

FIG. 10( a) is a perspective view of an example glucose sensor systemfor use in accordance with an embodiment.

FIG. 10( b) is a side cross-sectional view of a glucose sensor system ofFIG. 10( a) for an embodiment.

FIG. 10( c) is a perspective view of an example sensor set for a glucosesensor system of FIG. 10( a) for use in accordance with an embodiment.

FIG. 10( d) is a side cross-sectional view of a sensor set of FIG. 10(c) for an embodiment.

FIG. 11 is a cross sectional view of an example sensing end of a sensorset of FIG. 10( d) for use in accordance with an embodiment.

FIG. 12 is a top view of an example infusion device with a reservoirdoor in an open position, for use according to an embodiment.

FIG. 13 is a side view of an example infusion set with an insertionneedle pulled out, for use according to an embodiment.

DETAILED DESCRIPTION

As discussed above, contemporaneous measurements of a blood glucoseconcentration in a patient may be used for determining an insulintherapy for treating the patient's diabetic condition. Measurements of ablood glucose concentration may be useful in determining an insulintherapy for treating a diabetic's immediate condition. Measurements ofblood glucose alone, however, may not completely characterize thepatient's glycemic health, or take into consideration any possiblelong-term effects from application of a particular insulin therapy overa time period, for example.

In particular embodiments of insulin therapy infusion of insulin may becontrolled so as to control/maintain a patient's blood glucoseconcentration at a target level or within a target range, thus reducingthe risk that a patient's blood glucose level transition to dangerousextreme levels. Maintaining a patient's blood glucose concentration at atarget level or within a target range may reduce the risk ofhypoglycemia or hyperglycemia if a patient, non-medical professional ormedical professional is not fully attentive to acting to affect a systemfor effective glycemic management.

Depending on a patient's particular physiology, a target or set-pointglucose level may be established. For example, such a target orset-point glucose level may be defined based, at least in part, onguidelines established by the American Diabetes Association (ADA) and/orclinical judgment of a patient's physician. Here, for example, the ADAhas recommended a pre-prandial blood glucose concentration of between80-130 mg/dl, which is in the normal glycemic range. Alternatively,target or set-point glucose level may be fixed at 120 mg/dl, forexample. In yet another alternative, a target or set-point blood glucoseconcentration may vary over time depending on particular patientconditions. It should be understood, however, that these are merelyexamples of a target or set-point blood glucose concentration, andclaimed subject matter is not limited in this respect.

While techniques for establishing target level or target range for apatient's blood glucose concentration may consider immediate risks of apatient being in either a hypoglycemic or hyperglycemic state, thesetechniques typically do not consider longer-term effects of a patientbeing in a hypoglycemic or hyperglycemic state. As mentioned above, aconcentration of hemoglobin A_(1c) is typically used as a metric forglycemic health. However, concentration of hemoglobin A_(1c) in apatient is correlated with central tendency (e.g., average) bloodglucose concentration and not a statistical dispersion of blood glucoseconcentration (standard deviation). Concentration of hemoglobin A_(1c)in a patient does not quantify an extent of exposure to glycemicvariation (hypoglycemia and hyperglycemia). Other indicators indicativeof or correlated with hypoglycemic or hyperglycemic conditions mayinclude, for example, J-index, glycemic risk assessment diabetesequation (GRADE), M-value (e.g., using 100 mg/dl blood glucoseconcentration as an ideal), versions of the index of glycemic control(e.g., denoted in specific examples as IGC₁ and IGC₂ herein),coefficient of variation (CV), Kovatchev's low/high blood glucose index(LBGI, HBGI) and total risk index (RI=LBGI+HBGI), % Hypo (e.g., <70mg/dl), % Hyper (e.g., >140 mg/dl), AUC below 70 mg/dl, AUC above 140mg/dl, total AUC_(hypo), AUC_(hyper), AUC), interquartile range, andCameron et al.'s loss function. One or more of these metrics may beapplied to a closed-loop glycemic management system as shown in FraserCameron, B. Wayne Bequette, Darrell M. Wilson, Bruce A. Buckingham,Hyunjin Lee, and Günter Niemeyer “A Closed-Loop Artificial PancreasBased on Risk Management,” Journal of Diabetes Science and Technology,Volume 5, Issue 2, March 2011. From a virtual population of 10,000patients, a distribution of 4,000 patient-months of data was collected,a table of Spearman's rank correlation coefficients was created as shownin FIG. 1.

As can be observed, some metrics are strongly correlated withhyperglycemia, including mean blood glucose concentration and A_(1c).Mean blood glucose concentration and A_(1c) metrics are also shown to benegatively correlated with hypoglycemia. Accordingly, use of suchmetrics to assess glycemic control in an insulin infusion therapy maylead to a tendency toward increased hypoglycemia and/or decreasedhyperglycemia. Other metrics are strongly correlated with hypoglycemiaand negatively correlated with hyperglycemia. As discussed below in aparticular implementation, application of a log square cost function toa blood glucose concentration profile may lead to an insulin therapyproviding an improved balance of the immediate risks of hypoglycemia andlong-term risks of hyperglycemia.

In one aspect, particular embodiments described herein are directed totechniques for computing a metric enabling an improved control ofinsulin infusion so as to balance short-term risks and long-term risksassociated with particular insulin therapies. In one particularembodiment, a technique for computing such a metric may comprise:characterizing a profile of a patient's blood glucose concentration;applying one or more cost or loss functions to the blood glucoseconcentration profile to provide a unidimensional value representativeof glycemic health of the patient; and affecting a therapy applied tosaid patient based, at least in part, on the unidimensional value. In aparticular embodiment, recognizing that a patient's blood glucoseconcentration may be roughly statistically distributed according to alognormal distribution, a cost or loss function for application to ablood glucose concentration may be derived according to a log-squaremetric.

In one aspect, a unidimensional metric J may be computed as aconvolution of a function representing a profile of a patient's bloodglucose concentration with a cost or loss function to represent apatient's glycemic health in expression (1) as follows:

J =∫ _(−∞) ^(∞) g(x)f(x)dx,   (1)

where:

-   -   x is a patient's blood glucose concentration;    -   f(x) is a function representing a profile of the patient's blood        glucose concentration; and    -   g(x) is cost or loss function associated with the patient's        blood glucose concentration being at level x.

It should be understood that expression (1) provides merely a singlemathematical example of how a blood glucose concentration profile may beconvolved with a loss or cost function for computing a unidimensionalmetric and that other implementations (e.g., in a special purposecomputing apparatus programmed with instructions) may be employedwithout deviating from claimed subject matter.

Here, a unidimensional metric J may enable convenient assessment of apatient's health, and may allow for determination of an insulin infusiontherapy that balances short-term and long-term risks of hypoglycemia andhyperglycemia. Here, a unidimensional metric may comprise a single valuehaving a magnitude on a particular scale or range of values, such as anumerical value on a particular scale or range of numerical values. Inone particular example, a unidimensional metric may be expressed as apercentage, a value normalized to a range between 0.0 and 1.0, etc. In aparticular implementation, a value of J is an indication of glycemichealth where a low value represents relatively good or normal glycemichealth while a higher value represents poorer glycemic health. Inparticular implementations, f(x) may represent a patient's blood glucoseprofile over a longer time period (e.g., a month or longer) or just afew hours.

In one particular implementation, a f(x) may comprise a probabilitydensity function of a patient's blood glucose concentration which isdetermined or estimated using any one of several techniques. Inparticular example, a probability density function of patient's bloodglucose concentration may be constructed from a histogram of bloodglucose measurements taken over time using, for example, metered bloodglucose reference samples or measurements obtained from a blood glucosesensor in a continuous blood glucose monitoring system. Alternatively, aprobability density function of patient's blood glucose concentrationmay be modeled using any one of several functions used to model abiological process such as, for example, a normal distribution,lognormal distribution, just to name a couple of examples. Parametersfor these models of a probability density function of patient's bloodglucose concentration (e.g., mean, standard deviation) may be estimatedfrom past observations of the patient's blood glucose concentrationusing well known techniques.

In another particular implementation, a patient's blood glucoseconcentration profile f(x) may represent actual measurements bloodglucose concentration (e.g., from metered blood glucose measurements orsensor glucose measurements from a continuous glucose monitoring system)collected over a time period.

FIGS. 2 and 3 are example plots of a patient's blood glucoseconcentration laid over a loss or cost function that may be used tocompute unidimensional metric J in particular embodiments. In thisparticular example, FIG. 2, represents the patient's blood glucoseconcentration profile as a probability density function in plot 102.Plot 104 represents a quadratic loss or cost function. Here, plots 102and 104 are shown in a linear domain. Plot 102 is indicative of a skewedprobability density function which is not normal, and plot 104, ifapplied as g(x) as shown above in relation (1), gives significantly moreweight to hyperglycemia in computing metric J than it does tohypoglycemia. Alternatively, as illustrated in FIG. 3, plot 106 mayrepresent a patient's blood glucose concentration as a lognormalprobability density function. Plot 108, representing a quadratic loss orcost function, may be applied against the blood glucose concentrationprofile of plot 106.

To account for a skewed blood glucose concentration profile (e.g.,represented as a skewed probability distribution of a patient's bloodglucose concentration approaching a lognormal distribution asillustrated in FIGS. 2 and 3,), a loss or cost function g(x) to be usedin computing J in relation (1) may comprise a log-square metric formshown in expression (2) as follows:

$\begin{matrix}{{{g(x)} = \left\lbrack {\log \left( \frac{x}{G_{c}} \right)} \right\rbrack^{2}},} & (2)\end{matrix}$

-   -   Where G_(c) is a parameter that determines a local minimum or        center of a curve in a linear scaled domain.

In a particular implementation, G_(c) may be selected to according to anoptimization to balance Spearman rank coefficients with threehypoglycemic-biased metrics (% Hypo, LBGI and AUC_(hypo)) and threehyperglycemic-biased metrics (mean, % Hyper and HBGI). Using valuesshown in the particular example of the table shown in FIG. 5, a value ofG_(c) is selected as 120 mg/dl. In other implementations, a value ofG_(c) may be selected to balance different glycemic risks according todifferent metrics as shown as examples in Table 1 as follows:

TABLE 1 Metric Centering Value (G_(c)) Rank Correlation A1c  20 mg/dl99% GRADE  40 mg/dl 100% M-Value  45 mg/dl 99% Risk Index 115 mg/dl 99%Cameron et al. 115 mg/dl 100% AUC 120 mg/dl 97%

It should be understood that the particular values for G_(c) and rankcorrelation are merely example values presented for the purpose ofillustration, and that claimed subject matter is not limited to theseparticular values. For example, retrospective analysis, health careoutcome analysis, clinician subjective assessments, randomized clinicaltrials, and other sources of data can be leveraged to develop moreinsight around determining an optimal value for G_(c) for a patient orgroup of patients, etc.

In a recent exercise eight key opinion leaders (KOLs, e.g., physiciansand other clinicians) were asked to rank the order of priority in whichthey would prefer to see a group of five representative patients whichhaving had their blood glucose concentration continuously monitored withblood glucose sensors. The data was shown to them first as the A_(1c)and standard deviation of observed blood glucose concentration (SD).Then they were shown additional data including a probabilitydistribution indicating a duration of time that measured blood glucoseconcentration remained below 70 mg/dl, between 70 and 140 mg/dl, andabove 140 mg/dl. Finally, the KOLs where shown time series data forseven days of blood glucose sensor use for each patient. FIGS. 6( c) and6(d) show a representative population overlaid on quant plots forapplication of cost or loss function g(x) using a log-square metricaccording to expression (1), an isoquant for the 2.5-percentile equal to70 mg/dl, an isoquant for the 97.5-percentile equal to 200 mg/dl, andcontours for population distribution percentiles (25, 50, 75, and97.5%). KOL rankings correlated well with the log-square metric(Spearman's rank correlation of 0.7). KOL rankings also indicated a biasto treat hypoglycemia first, giving a patient with a median glucose ofalmost 200 mg/dl the third place instead of the first place as alog-square ranking may indicate. The KOL ranking may reflect an urgencyto treat the immediate threat of hypoglycemia, and not necessarilyaddress the quality of glycemic control. Sustained hyperglycemia asexhibited by subject 3 in the KOL ranking indicates poor glycemiccontrol (possibly leading to long-term health risks), but it does notimply an immediate threat to the patient's life (as with hypoglycemia).

Other alternative cost or loss functions applicable for g(x) in relation(1) are shown in the table of FIG. 4. It should be understood, however,that these are merely examples of functions that may provide a cost orloss function for application to a profile of a patient's blood glucoseconcentration in determining a metric representative of glycemic health,and claimed subject matter is not limited in this respect.

FIG. 7 is a schematic diagram of a system 50 comprising a computingenvironment according to an embodiment for use in determining andapplying a unidimensional metric for indicating glycemic health.Computing platforms 52 may be communicatively coupled to computingplatform 56 through network 58. Computing platforms 52 and 56 may havecommunication interface components to facilitate communication withother devices through network 58 including, for example, modems networkadapters and/or the like. Network 58 may comprise any one of severalcombinations of wired and wireless communication infrastructureincluding, for example, wired and wireless wide area networkinfrastructure and/or local area network infrastructure. In a particularimplementation, network 58 may provide Internet protocol infrastructureto facilitate communication between computing platform 56 and computingplatforms 52 in TCP/IP sessions, HTML, XML or other web serviceparadigms, for example.

Computing platforms 52 and 56 may comprise processors, memory,input/output devices, display devices, etc., to enable or supportapplications. For example, a memory may store instructions that areexecutable by a processor to perform one or more functions, tasks,processes, etc. In particular implementations, computing platforms 52and 56 may comprise any one of several types of computing devices suchas, for example, a server, personal computing, notebook computer, cellphone, smart phone, just to provide a few examples. Computing platforms52 and 56 may comprise a graphical user interface (GUI) that facilitatesuser interaction with applications.

In a particular implementation, computing platforms 52 may becommunicatively coupled (e.g., wired or wirelessly) to blood glucosemonitoring device 54 to receive measurements of a patient's bloodglucose concentration. Blood glucose monitoring device 54 may comprise ablood glucose meter capable of receiving blood glucose samples (e.g.,from test strips). In another embodiment, blood glucose monitoringdevice 54 may comprise a blood glucose sensor and monitor for providingcontinuous blood glucose concentration measurements from processingsignals from a blood glucose sensor as described below in a particularimplementation with reference to FIGS. 8 through 11.

Computing platforms 52 may be coupled to corresponding blood glucosemonitoring devices 54 using a wired or wireless link such as, forexample, a universal serial bus, Bluetooth link, ultra wideband link,IEEE Std. 802.11 link, just to provide a few examples. In one example, amonitoring device 54 may comprise a memory (not shown) to store ahistory of blood glucose concentration measurements to be downloaded toa computing platform 52. Alternatively, a blood glucose monitoringdevice 54 may forward blood glucose concentration measurements to acomputing platform 52 as such blood glucose measurements are received inreal-time.

In one implementation, system 50 may be located in a hospitalenvironment where computing platforms 52 are co-located with patients atdifferent locations communicate with a central computing platform 56 tocentrally collect and process patient data. In another implementation,system 50 may be more geographically distributed in that centralcomputing platform 50 may be located in doctor's office or medicalclinic while computing platforms 52 are located in patients' homes.Here, a unidimensional metric J may be computed for each patient toassess the patient's glycemic health. In one implementation, thecentrally computed unidimensional metrics may allow for tailoringinsulin therapies to the particular needs of the patients. In anotherimplementation, the centrally computed unidimensional metrics may beincorporating in a process for triaging patients so that patients havingthe most severe conditions are treated with a priority over patientswith least severe conditions. For example, patients with a highercomputed unidimensional metric J may be treated with priority overpatients with a lower computed unidimensional metric J. In anotherimplementation, computing platforms 52 may have sufficient softwareprogram resources to compute a unidimensional metric J based, at leastin part, on blood glucose measurements collected for a single patient ata blood glucose monitoring device 54 and without interaction with acentral computing platform 56.

In another embodiment, unidimensional metric J may be computed using abrief history of blood glucose measurements (e.g., over a three hourperiod) to obtain a snapshot of a patient's glycemic health. Here, apatient's glycemic health may be evaluated for different recurringconditions, time of day, time of week, etc. For example, a diabeticpatient's dietary or exercise habits during typical week days may bedifferent from the patient's dietary or exercise habits on the weekend,affecting the patient's glycemic health. It may be of interest, forexample, to assess the patient's glycemic health on particularperiodically repeating periods such as, for example, time of day (e.g.,morning before first meal, afternoon following lunch time, in theevening before or after bedtime, etc.), day of week (e.g., Mondaysfollowing a weekend of altered exercise and dietary habits), time ofmonth, time of year (e.g., during or following the holiday season), justto provide a few example of periodically repeating periods.

In one particular example, a unidimensional metric J to indicateglycemic health over a periodically repeating period may be computedbased, at least in part, on a profile f(x) based, at least in part, onblood glucose concentration measurements as discussed above (e.g.,either from metered blood glucose samples or from a blood glucose sensorin a continuous blood glucose monitoring system) collected over theperiodically repeating period. For computing unidimensional metric J forMondays, in a particular example, a patient's blood glucoseconcentration profile f(x) may comprise blood glucose concentrationmeasurements collected over the 24-hour period on Mondays over multipleweeks. Similarly, for computing unidimensional metric J for a time ofday (e.g., from 9 am to noon), a patient's blood glucose concentrationprofile f(x) may comprise blood glucose concentration measurementscollected during this particular time over multiple days. Similarly, forcomputing unidimensional metric J for a time of year (e.g., holidayperiod from late November to early January), a patient's blood glucoseconcentration profile f(x) may comprise blood glucose concentrationmeasurements collected during this time of the year over multiple years.Here, a patient's blood glucose concentration profile may be determinedfrom blood glucose concentration measurements or observations limited tosuch measurements or observations obtained during the particularperiodically repeating period of interest.

In one particular implementation, a blood glucose concentration profilef(x) characterizing blood glucose concentration for use in computing ametric may be expressed as follows:

f(x)=f(x|ξ), where ξ represents a particular recurring condition orperiod of interest.

Here, f(x|ξ) may represent a profile of a patient's blood glucoseconcentration profile based only on observations of the patient's bloodglucose concentration obtained during recurring condition period ξ. Forexample, as discussed above, f(x|ξ) may comprise an estimatedprobability distribution or a histogram derived from observations of thepatient's blood glucose concentration obtained during recurringcondition period ξ, or merely measurements of the patient's bloodglucose concentration obtained during recurring condition period ξ.

As discussed above, unidimensional metric J may be computed according toa cost or loss function g(x) so as to indicate a patient's glycemichealth based, at least in part, on factors or risks indicative ofhypoglycemia or hyperglycemia. Application of a particular cost or lossfunction g(x) as a log-square metric as shown in expression (2) mayquantify effects of a blood glucose concentration being in ahypoglycemic or hyperglycemic region (e.g., being below or above G_(c)).While providing a convenient indicator of a patient's glycemic health,application of the log-square metric shown in expression (2) as loss orcost function g(x) in expression (1) to compute J, by itself, may notindicate whether a relatively high value for J (indicating a diminishedglycemic health, for example) is brought about by hypoglycemia,hyperglycemia, or a mixture of hypoglycemic and hyperglycemic.

In one implementation, a “one sided” loss or cost function may beselectively applied for isolating and quantifying effects ofhyperglycemia to the exclusion of effects of hypoglycemia, orquantifying effects of hypoglycemia to the exclusion of effects ofhyperglycemia. Here, application of g(x) according to expression (3)below may isolate and quantify effects of hyperglycemia to the exclusionof effects of hypoglycemia:

$\begin{matrix}\begin{matrix}{{{g(x)} = \left\lbrack {\log \left( \frac{x}{G_{c}} \right)} \right\rbrack^{2}},{{{for}\mspace{14mu} x} \geq G_{c}}} \\{{= 0},{{{for}\mspace{14mu} x} < {G_{c}.}}}\end{matrix} & (3)\end{matrix}$

By convolving a patient's blood glucose concentration profile f(x) inexpression (1) to compute metric J with g(x) as provided in a one-sidedloss or cost function according to either expression (3), we can computeand quantify the effect of hyperglycemia in isolation. Similarly,application of g(x) according to expression (4) below may isolate andquantify effects of hypoglycemia to the exclusion of effects ofhyperglycemia:

$\begin{matrix}\begin{matrix}{{{g(x)} = \left\lbrack {\log \left( \frac{x}{G_{c}} \right)} \right\rbrack^{2}},{{{for}\mspace{14mu} x} \leq G_{c}}} \\{{= 0},{{{for}\mspace{14mu} x} > {G_{c}.}}}\end{matrix} & (4)\end{matrix}$

By convolving a patient's blood glucose concentration profile f(x) inexpression (1) to compute metric J with g(x) as provided in a one-sidedloss or cost function according to either expression (4), we can computeand quantify the effect of hypoglycemia in isolation. While theparticular examples of a one-sided loss or cost function in expressions(3) and (4) employ a log-square metric loss or cost function, it shouldbe understood that such an application of a log-square metric ispresented merely as an example, and that a one-sided loss or costfunction may be derived from different expressions without deviatingfrom claimed subject matter.

As discussed above, computation of unidimensional metric J mayfacilitate evaluation of a patient's glycemic health for differentrecurring conditions, time of day, time of week, etc. While convolving apatient's blood glucose concentration profile f(x) with cost or lossfunction g(x) according to relation (2) may identify recurringconditions, time of day, time of week, etc., where glycemic health isaffected (e.g., as indicated by a high number for J over a period inquestion), this computation of J may not be indicative of eitherhyperglycemia or hypoglycemia. In one implementation, in periods orconditions under which a computed metric J indicates a decline inglycemic health for a particular blood glucose concentration profilef(x), the particular blood glucose concentration profile f(x) may beconvolved again separately with one-sided loss or cost functions g(x) asset forth in expressions (3) and (4). The results of convolutions off(x) separately with loss or cost functions g(x) as set forth inexpressions (3) and (4) may be indicative of the effects ofhyperglycemia in isolation and hypoglycemia, respectively.

In another implementation, a particular blood glucose concentrationprofile f(x) may be convolved separately with loss or cost functionsg(x) as set forth in expressions (3) and (4) to provide a bi-dimensionalmetric indicative of the effects of hyperglycemia and hypoglycemia,separately, in two distinct dimensions. For example, two valuesJ_(Hyper) and J_(Hypo), indicating glycemic health or risk in ahyperglycemia and hypoglycemia dimensions, respectively, may be computedusing a log-square metric cost or loss function in expression (5) asfollows:

$\begin{matrix}{{J_{Hyper} = {\int_{G_{c}}^{\infty}{\left\lbrack {\log \left( \frac{x}{G_{c}} \right)} \right\rbrack^{2}{f(x)}{x}}}},{and}} & (5) \\{J_{Hypo} = {\int_{- \infty}^{G_{c}}{\left\lbrack {\log \left( \frac{x}{G_{c}} \right)} \right\rbrack^{2}{f(x)}{{x}.}}}} & \;\end{matrix}$

While expression (5) applies a log-square loss or cost function, itshould be understood that other loss or cost functions may be usedwithout deviating from claimed subject matter. Separately identifyingeffects of hyperglycemic and hypoglycemic conditions contributing toglycemic health according to expression (5) may enable more effectivetherapies for managing a patient's glycemic health. As a discussedabove, unidimensional metric J may be computed for a patient's bloodglucose concentration profile f(x) over different recurring conditions,times of day, day of week, time of year, etc. Application of a one-sidedloss or cost function g(x) (e.g., as in expression (3) or (4)) incomputing J or application of a bi-dimensional metric may allow forfurther identification of how glycemic health is being effected in thesedifferent recurring conditions, times of day, day of week, time of year,etc. (e.g., by hypoglycemia or hypoglycemia). By identifying a patientas experiencing either hyperglycemia or hypoglycemia for a particularrecurring condition, time of day, day of week, time of year, etc, apatient's therapy or behavior may be altered to address the particularhyperglycemic or hypoglycemic state. To counteract a particularhyperglycemic or hypoglycemic state for a particular recurringcondition, for example, a patient may alter diet, exercise or insulintherapy (e.g., insulin infusion basal rate), or reduce stress, just toname a few examples.

In a particular implementation, a bi-dimensional metric may be used toadjust a patient's target blood glucose concentration level or range.For example, as described in U.S. patent application Ser. No.12/820,944, filed on Jun. 22, 2010 and assigned to the assignee ofclaimed subject matter, a closed-loop blood glucose monitoring andinsulin deliver system may establish a target blood glucoseconcentration level or target range. This target range may be adjustedupward, for example, if the patient is tending to be hypoglycemic for aparticular recurring period, or adjusted downward if the patient istending to be hyperglycemic. Such an adjustment of a target glucoselevel or target range may allow for a better balance of the immediaterisks of a hypoglycemic condition with the longer-term risks of ahyperglycemic condition.

FIG. 8 is a block diagram of an example closed loop glucose controlsystem 105 in accordance with an embodiment. Particular embodiments mayinclude a glucose sensor system 110, a controller 112, an insulindelivery system 114, and a glucagon delivery system 115, etc. as shownin FIG. 8. In certain example embodiments, glucose sensor system 110 maygenerate a sensor signal 116 representative of blood glucose levels 118in body 120, and glucose sensor system 110 may provide sensor signal 116to controller 112. Controller 112 may receive sensor signal 116 andgenerate commands 122 that are communicated at least to insulin deliverysystem 114 and/or glucagon delivery system 115. Insulin delivery system114 may receive commands 122 and infuse insulin 124 into body 120 inresponse to commands 122. Likewise, glucagon delivery system 115 mayreceive commands 122 from controller 112 and infuse glucagon 125 intobody 120 in response to commands 122.

Glucose sensor system 110 may include, by way of example but notlimitation, a glucose sensor; sensor electrical components to providepower to a glucose sensor and to generate sensor signal 116; a sensorcommunication system to carry sensor signal 116 to controller 112; asensor system housing for holding, covering, and/or containingelectrical components and a sensor communication system; any combinationthereof, and so forth.

Controller 112 may include, by way of example but not limitation,electrical components, other hardware, firmware, and/or software, etc.to generate commands 122 for insulin delivery system 114 and/or glucagondelivery system 115 based at least partly on sensor signal 116.Controller 112 may also include a controller communication system toreceive sensor signal 116 and/or to provide commands 122 to insulindelivery system 114 and/or glucagon delivery system 115. In particularexample implementations, controller 112 may include a user interfaceand/or operator interface (not shown) comprising a data input deviceand/or a data output device. Such a data output device may, for example,generate signals to initiate an alarm and/or include a display orprinter for showing a status of controller 112 and/or a patient's vitalindicators, monitored historical data, combinations thereof, and soforth. Such a data input device may comprise dials, buttons, pointingdevices, manual switches, alphanumeric keys, a touch-sensitive display,combinations thereof, and/or the like for receiving user and/or operatorinputs. It should be understood, however, that these are merely examplesof input and output devices that may be a part of an operator and/oruser interface and that claimed subject matter is not limited in theserespects.

Insulin delivery system 114 may include an infusion device and/or aninfusion tube to infuse insulin 124 into body 120. Similarly, glucagondelivery system 115 may include an infusion device and/or an infusiontube to infuse glucagon 125 into body 120. In alternative embodiments,insulin 124 and glucagon 125 may be infused into body 120 using a sharedinfusion tube. In other alternative embodiments, insulin 124 and/orglucagon 125 may be infused using an intravenous system for providingfluids to a patient (e.g., in a hospital or other medical environment).While an intravenous system is employed, glucose may be infused directlyinto a bloodstream of a body instead of or in addition to infusingglucagon into interstitial tissue. It should also be understood thatcertain example embodiments for closed loop glucose control system 105may include an insulin delivery system 114 without a glucagon deliverysystem 115 (or vice versa).

In particular example embodiments, an infusion device (not explicitlyidentified in FIG. 8) may include electrical components to activate aninfusion motor according to commands 122; an infusion communicationsystem to receive commands 122 from controller 112; an infusion devicehousing (not shown) to hold, cover, and/or contain the infusion device;any combination thereof; and so forth.

In particular example embodiments, controller 112 may be housed in aninfusion device housing, and an infusion communication system maycomprise an electrical trace or a wire that carries commands 122 fromcontroller 112 to an infusion device. In alternative embodiments,controller 112 may be housed in a sensor system housing, and a sensorcommunication system may comprise an electrical trace or a wire thatcarries sensor signal 116 from sensor electrical components tocontroller electrical components. In other alternative embodiments,controller 112 may have its own housing or may be included in asupplemental device. In yet other alternative embodiments, controller112 may be co-located with an infusion device and a sensor system withinone shared housing. In further alternative embodiments, a sensor, acontroller, and/or infusion communication systems may utilize a cable; awire; a fiber optic line; RF, IR, or ultrasonic transmitters andreceivers; combinations thereof; and/or the like instead of electricaltraces, just to name a few examples.

FIGS. 9 through 13 illustrate example glucose control systems inaccordance with certain embodiments. FIG. 9 is a front view of exampleclosed loop hardware located on a body in accordance with certainembodiments. FIGS. 10( a)-10(d) and 11 show different views and portionsof an example glucose sensor system for use in accordance with certainembodiments. FIG. 12 is a top view of an example infusion device with areservoir door in an open position in accordance with certainembodiments. FIG. 13 is a side view of an example infusion set with aninsertion needle pulled out in accordance with certain embodiments.

Particular example embodiments may include a sensor 126, a sensor set128, a telemetered characteristic monitor 130, a sensor cable 132, aninfusion device 134, an infusion tube 136, and an infusion set 138, anyor all of which may be worn on a body 120 of a user or patient, as shownin FIG. 9. As shown in FIGS. 10( a) and 10(b), telemeteredcharacteristic monitor 130 may include a monitor housing 131 thatsupports a printed circuit board 133, battery or batteries 135, antenna(not shown), a sensor cable connector (not shown), and so forth. Asensing end 140 of sensor 126 may have exposed electrodes 142 that maybe inserted through skin 146 into a subcutaneous tissue 144 of a user'sbody 120, as shown in FIGS. 10( d) and 11. Electrodes 142 may be incontact with interstitial fluid (ISF) that is usually present throughoutsubcutaneous tissue 144.

Sensor 126 may be held in place by sensor set 128, which may beadhesively secured to a user's skin 146, as shown in FIGS. 10( c) and10(d). Sensor set 128 may provide for a connector end 27 of sensor 26 toconnect to a first end 129 of sensor cable 132. A second end 137 ofsensor cable 132 may connect to monitor housing 131. Batteries 135 thatmay be included in monitor housing 131 provide power for sensor 126 andelectrical components 139 on printed circuit board 133. Electricalcomponents 139 may sample a current of sensor signal 116 (e.g., of FIG.8) to provide digital sensor values (Dsig) and store Dsig values in amemory. Digital sensor values Dsig may be periodically transmitted froma memory to controller 112, which may be included in an infusion device.

In a particular implementation, controller 112 may perform additionalfiltering and processing on values for Dsig to compute continuous sensorblood glucose measurements as described in U.S. patent application Ser.No. 12/345,477, filed on Dec. 29, 2008, and Ser. No. 12/347,716, filedon Dec. 31, 2008, assigned to the assignee of claimed subject matter andincorporated herein by reference. These continuous blood glucosemeasurements may then be used for determining a patient's blood glucoseconcentration profile f(x) for use in computing unidimensional metric Jas set forth in expression (1) above, for example. For example, thesesensor blood glucose measurements may themselves be directly convolvedwith g(x) to produce J according to relation (1). Alternatively, sensorblood glucose measurements may be used to estimate parameters of aprobability density function modeling a patient's blood glucoseconcentration.

With reference to FIGS. 8, 9 and 12, a controller 112 may processdigital sensor values Dsig and generate commands 122 for infusion device134. Infusion device 134 may respond to commands 122 and actuate aplunger 148 that forces insulin 124 out of a reservoir 150 that islocated inside an infusion device 134. Glucose may be infused from areservoir responsive to commands 122 using a similar and/or analogousdevice (not shown). In alternative implementations, glucose may beadministered to a patient orally.

In particular example embodiments, a connector tip 154 of reservoir 150may extend through infusion device housing 152, and a first end 151 ofinfusion tube 136 may be attached to connector tip 154. A second end 153of infusion tube 136 may connect to infusion set 138 (e.g., of FIGS. 9and 13). Insulin 124 may be forced through infusion tube 136 intoinfusion set 138 and into body 116. Infusion set 138 may be adhesivelyattached to a user's skin 146. As part of infusion set 138, a cannula156 may extend through skin 146 and terminate in subcutaneous tissue 144to complete fluid communication between a reservoir 150 and subcutaneoustissue 144 of a user's body 116.

In one implementation, a unidimensional (e.g., J) or bi-dimensionalmetric (e.g., J_(Hypo) in combination with J_(Hyper)) may be used inreal-time control of a closed-loop system (e.g., as shown in FIG. 8) toprovide insulin and/or glucagon. For example, a controller 112 maycompute a control signal on a periodic basis to be used in formulatingcommands for a pump to infuse insulin and/or glucagon. Here, applicationof a unidimensional or bi-dimensional metric in formulating real-timecommands in a closed loop system may allow for an improved balancing ofthe immediate risk of hypoglycemia and the long-term risks ofhyperglycemia, for example.

In another example implementation, a unidimensional or bi-dimensionalmetric as discussed above may be used for triggering an alarm for apatient under certain conditions. As discussed above, during particulartimes of day, days of week, etc. a patient may be likely to encountergreater risks hyperglycemia or hypoglycemia than other times of day,days of week, etc. Under such a condition, an alarm signal may betriggered if a unidimensional or bi-dimensional metric as discussedabove exceeds a threshold value. The alarm signal may initiate anaudible or visual signal to cue the patient, or automatically initiate achange in therapy provided by a closed-loop system, for example.

In yet another example implementation, a unidimensional orbi-dimensional metric as discussed above may be used for performingoptimization studies for the evaluation and design of control algorithms(e.g., control algorithms such as a closed-loop system for providinginsulin and/or glucagon based, at least in part, on sensor glucosemeasurements. Here, a unidimensional or bi-dimensional metric may beconveniently computed over data sets covering large numbers individuals.The different therapies may then be ranked based upon an average valueof J, for example.

In example alternative embodiments, as pointed out above, a closed-loopsystem in particular implementations may be a part of a hospital-basedglucose management system. Given that insulin therapy during intensivecare has been shown to dramatically improve wound healing and reduceblood stream infections, renal failure, and polyneuropathy mortality,irrespective of whether subjects previously had diabetes (See, e.g., Vanden Berghe G. et al. NEJM 345: 1359-67, 2001), particular exampleimplementations may be used in a hospital setting to control a bloodglucose level of a patient in intensive care. In such alternativeembodiments, because an intravenous (IV) hookup may be implanted into apatient's arm while the patient is in an intensive care setting (e.g.,ICU), a closed loop glucose control may be established that piggy-backsoff an existing IV connection. Thus, in a hospital or othermedical-facility based system, IV catheters that are directly connectedto a patient's vascular system for purposes of quickly delivering IVfluids, may also be used to facilitate blood sampling and directinfusion of substances (e.g., insulin, glucose, anticoagulants, etc.)into an intra-vascular space.

Moreover, glucose sensors may be inserted through an IV line to provide,e.g., real-time glucose levels from the blood stream. Therefore,depending on a type of hospital or other medical-facility based system,such alternative embodiments may not necessarily utilize all of thedescribed system components. Examples of components that may be omittedinclude, but are not limited to, sensor 126, sensor set 128, telemeteredcharacteristic monitor 130, sensor cable 132, infusion tube 136,infusion set 138, and so forth. Instead, standard blood glucose metersand/or vascular glucose sensors, such as those described in co-pendingU.S. Patent Application Publication No. 2008/0221509 (U.S. patentapplication Ser. No. 12/121,647; to Gottlieb, Rebecca et al.; entitled“MULTILUMEN CATHETER”), filed 15 May 2008, may be used to provide bloodglucose values to an infusion pump control, and an existing IVconnection may be used to administer insulin to an patient. Otheralternative embodiments may also include fewer, more, and/or differentcomponents than those that are described herein and/or illustrated inthe accompanying Drawings.

Controller 112, and computing devices 52 and 56 may comprise one or moreprocessors capable of executing instructions to thereby rendercontroller 112, or computing devices 52 and 56 a special purposecomputing device to perform algorithms, functions, methods, etc.; toimplement attributes, features, etc.; and so forth that are describedherein. Such processor(s) may be realized as microprocessors, digitalsignal processors (DSPs), application specific integrated circuits(ASICs), programmable logic devices (PLDs), controllers,micro-controllers, a combination thereof, and so forth, just to name afew examples. Alternatively, an article may comprise at least onestorage medium (e.g., such as one or more memories) having storedthereon instructions 1706 that are executable by one or more processors.

Unless specifically stated otherwise, as is apparent from the precedingdiscussion, it is to be appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “assessing”, “estimating”, “identifying”,“obtaining”, “representing”, “receiving”, “transmitting”, “storing”,“analyzing”, “measuring”, “detecting”, “controlling”, “delaying”,“initiating”, “providing”, “performing”, “generating”, “altering” and soforth may refer to actions, processes, etc. that may be partially orfully performed by a specific apparatus, such as a special purposecomputer, special purpose computing apparatus, a similar special purposeelectronic computing device, and so forth, just to name a few examples.In the context of this specification, therefore, a special purposecomputer or a similar special purpose electronic computing device may becapable of manipulating or transforming signals, which are typicallyrepresented as physical electronic and/or magnetic quantities withinmemories, registers, or other information storage devices; transmissiondevices; display devices of a special purpose computer; or similarspecial purpose electronic computing device; and so forth, just to namea few examples. In particular example embodiments, such a specialpurpose computer or similar may comprise one or more processorsprogrammed with instructions to perform one or more specific functions.Accordingly, a special purpose computer may refer to a system or adevice that includes an ability to process or store data in the form ofsignals. Further, unless specifically stated otherwise, a process ormethod as described herein, with reference to flow diagrams orotherwise, may also be executed or controlled, in whole or in part, by aspecial purpose computer.

It should be understood that aspects described above are examples onlyand that embodiments may differ there from without departing fromclaimed subject matter. Also, it should be noted that although aspectsof the above systems, methods, apparatuses, devices, processes, etc.have been described in particular orders and in particular arrangements,such specific orders and arrangements are merely examples and claimedsubject matter is not limited to the orders and arrangements asdescribed. It should additionally be noted that systems, devices,methods, apparatuses, processes, etc. described herein may be capable ofbeing performed by one or more computing platforms.

In addition, instructions that are adapted to realize methods,processes, etc. that are described herein may be capable of being storedon a storage medium as one or more machine readable instructions. Ifexecuted, machine readable instructions may enable a computing platformto perform one or more actions. “Storage medium” as referred to hereinmay relate to media capable of storing information or instructions whichmay be operated on, or executed by, one or more machines (e.g., thatinclude at least one processor). For example, a storage medium maycomprise one or more storage articles and/or devices for storingmachine-readable instructions or information. Such storage articlesand/or devices may comprise any one of several media types including,for example, magnetic, optical, semiconductor, a combination thereof,etc. storage media. By way of further example, one or more computingplatforms may be adapted to perform one or more processes, methods, etc.in accordance with claimed subject matter, such as methods, processes,etc. that are described herein. However, these are merely examplesrelating to a storage medium and a computing platform and claimedsubject matter is not limited in these respects.

Although there have been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from central concepts that are described herein. Therefore, itis intended that claimed subject matter not be limited to particularexamples disclosed, but that such claimed subject matter may alsoinclude all aspects falling within the scope of appended claims, andequivalents thereof.

1. A method comprising: at a special purpose computing apparatus,computing a profile of a blood glucose concentration of a patient based,at least in part, on observations of said blood glucose concentrationcollected at a blood glucose monitoring device; applying a cost or lossfunction to said computed profile to compute a metric representative ofa glycemic health of the patient, said cost or loss function beingbased, at least in part, on a log-square metric; and affecting a therapyapplied to said patient based, at least in part, on said computedmetric.
 2. The method of claim 1, wherein said metric comprises aunidimensional metric.
 3. The method of claim 1, wherein affecting saidtherapy comprises setting a target blood glucose level or target bloodglucose range of said patient based, at least in part, on said computedmetric.
 4. The method of claim 1, wherein said blood glucoseconcentration profile comprises an estimated probability distributionfunction.
 5. The method of claim 1, wherein said blood glucoseconcentration profile comprises a histogram of blood glucoseconcentration measurements.
 6. The method of claim 1, wherein said bloodglucose concentration profile comprises blood glucose concentrationmeasurements.
 7. The method of claim 1, wherein said computed metriccomprises a bi-dimensional metric, and further comprising: applying afirst portion of said loss or cost function to said blood glucoseconcentration profile to derive a hyperglycemia component of saidcomputed metric; and applying a second portion of said loss or costfunction to said blood glucose concentration to derive a hypoglycemiacomponent of said computed metric.
 8. The method of claim 1, whereinsaid log-square metric has the form${g(x)} = {\left\lbrack {\log_{2}\left( \frac{x}{G_{c}} \right)} \right\rbrack^{2}.}$9. The method of claim 1, and further comprising: defining the bloodglucose concentration profile for a recurring period; computing saidmetric based, at least in part, said recurring period; and affectingsaid therapy for said recurring period in the future based, at least inpart, on said computed metric.
 10. The method of claim 9, wherein saidrecurring period comprises a time of day.
 11. The method of claim 9,wherein said recurring period comprises a day of week.
 12. The method ofclaim 9, wherein said defining said blood glucose concentration profilefor a recurring period further comprises computing said profile for therecurring period based on observations of said blood glucoseconcentration collected only during said recurring period.
 13. Themethod of claim 1, wherein affecting the therapy applied to said patientfurther comprises affecting a closed-loop insulin delivery system based,at least in part, on said computed metric.
 14. The method of claim 13,wherein affecting said closed-loop insulin delivery system furthercomprises affecting a periodic command based, at least in part, on saidcomputed metric.
 15. The method of claim 1, and further comprisingtriggering an alarm in response to said computed metric.
 16. Anapparatus comprising: one or more processors to: for a plurality ofpatients, determine an associated plurality of unidimensional metricsindicative of glycemic health of the patients, said unidimensionalmetrics being computed based, at least in part, on an application of acost or loss function to profiles of blood glucose concentration of saidpatients; and rank said patients for treatment according to a triagepolicy based, at least in part, on said unidimensional metrics.
 17. Theapparatus of claim 16, and further comprising communication interfacecomponents to receive information from a communication network, said oneor more processors further to: compute said unidimensional metricsbased, at least in part, on messages received through said communicationinterface components from computing platforms co-located with saidpatients.
 18. The apparatus of claim 17, wherein said messages comprisemeasurements of blood glucose concentration collected at glucosemonitoring devices.
 19. The apparatus of claim 17, wherein said messagescomprise unidimensional metrics computed at said computing platformsco-located with said patients.
 20. An article comprising: anon-transitory storage medium comprising machine-readable instructionsstored thereon which are executable by a special purpose computingapparatus to: determine an associated plurality of unidimensionalmetrics indicative of glycemic health of the patients, saidunidimensional metrics being computed based, at least in part, on anapplication of a cost or loss function to profiles of blood glucoseconcentration of said patients; and rank said patients for treatmentaccording to a triage policy based, at least in part, on saidunidimensional metrics.
 21. An article comprising: a non-transitorystorage medium comprising machine-readable instructions stored thereonwhich are executable by a special purpose computing apparatus to:compute a profile of a blood glucose concentration of a patient based,at least in part, on observations of said blood glucose concentrationcollected at a blood glucose monitoring device; apply a cost or lossfunction to said computed profile to compute a metric representative ofa glycemic health of the patient, said cost or loss function beingbased, at least in part, on a log-square metric; and affect a therapyapplied to said patient based, at least in part, on said computedmetric.
 22. The article of claim 21, wherein said instructions areexecutable by said special purpose computing apparatus to affect saidtherapy by generating commands in an infusion system.
 23. An articlecomprising: a non-transitory storage medium comprising machine-readableinstructions stored thereon which are executable by a special purposecomputing apparatus to: for a plurality of patients, determine anassociated plurality of unidimensional metrics indicative of glycemichealth of the patients, said unidimensional metrics being computedbased, at least in part, on an application of a cost or loss function toprofiles of blood glucose concentration of said patients; and rank saidpatients for treatment according to a triage policy based, at least inpart, on said unidimensional metrics.
 24. An apparatus comprising: meansfor computing a profile of a blood glucose concentration of a patientbased, at least in part, on observations of said blood glucoseconcentration; means for applying a cost or loss function to saidcomputed profile to compute a metric representative of a glycemic healthof the patient, said cost or loss function being based, at least inpart, on a log-square metric; and means for affecting a therapy appliedto said patient based, at least in part, on said computed metric.
 25. Anapparatus comprising: for a plurality of patients, means for determiningan associated plurality of unidimensional metrics indicative of glycemichealth of the patients, said unidimensional metrics being computedbased, at least in part, on an application of a cost or loss function toprofiles of blood glucose concentration of said patients; and means forranking said patients for treatment according to a triage policy based,at least in part, on said unidimensional metrics.
 26. A methodcomprising: at a special purpose computing apparatus, for a plurality ofpatients, determining an associated plurality of unidimensional metricsindicative of glycemic health of the patients, said unidimensionalmetrics being computed based, at least in part, on an application of acost or loss function to profiles of blood glucose concentration of saidpatients, said profiles of blood glucose concentration being obtainedfor said patients under multiple predefined therapies; and ranking saidpredefined therapies based, at least in part, on said unidimensionalmetrics.
 27. The method of claim 26, wherein said predefined therapiesare defined, at least in part, by closed-loop system design features.